Search Results (405)

Understanding the interplay between lightning activity and hydrometeor distribution is crucial for advancing knowledge of thunderstorm electrification processes. Using three-dimensional lightning mapping and dual-polarization radar observations, this study investigates the spatiotemporal correlations between low-frequency (LF) lightning sources and hydrometeors during a severe thunderstorm on 11 June 2014, in North Carolina, USA. The results reveal that lightning sources are predominantly observed above 6 km (near the −10 °C isotherm) and stabilize into a dual-peak vertical distribution as the storm progresses into its mature stage, with peaks located at 6–7 km (−10 °C to −15 °C) and 10–11 km (approximately −40 °C). Low-density graupel (LDG) and aggregates (AGs) dominate at lightning locations. Stronger updrafts lead to higher proportions of LDG and high-density graupel (HDG), and lower proportions of AG. LDG exhibits the strongest positive correlation with LF lightning sources, with a peak correlation coefficient of 0.65 at 9 km. During the vigorous development stage, HDG and hail (Ha) also show positive correlations with LF lightning sources, with peak correlation coefficients of 0.52 at 7 km and 0.42 at 8 km, respectively. As the storm reaches its mature phase, the correlation between LDG and lightning sources also displays a dual-peak vertical distribution, with peaks at 7–8 km and 13–14 km. Both the peak correlation coefficient and its corresponding height increase with the strengthening of updrafts, underscoring the critical role of updrafts in microphysical characteristics and driving electrification processes. Full article

To advance the development of protein-rich plant-based foods, a novel binder system for vegan sausage alternatives without the requirement of heat application was investigated. This enables long-term ripening of plant-based analogs similar to traditional fermented meat or dairy products, allowing for refined flavor and texture development. This was achieved by using a poorly water-soluble calcium source (calcium carbonate) to introduce calcium ions into a low-ester pectin—gluten matrix susceptible to crosslinking via divalent ions. The gelling reaction of pectin–gluten dispersions with Ca²⁺ ions was time-delayed due to the gradual production of lactic acid during fermentation. Firm, sliceable matrices were formed, in which particulate substances such as texturized proteins and solid vegetable fat could be integrated, hence forming an unheated raw-fermented plant-based salami-type sausage model matrix which remained safe for consumption over 21 days of ripening. Gluten as well as pectin had a significant influence on the functional properties of the matrices, especially water holding capacity (increasing with higher pectin or gluten content), hardness (increasing with higher pectin or gluten content), tensile strength (increasing with higher pectin or gluten content) and cohesiveness (decreasing with higher pectin or gluten content). A combination of three simultaneously occurring effects was observed, modulating the properties of the matrices, namely, (a) an increase in gel strength due to increased pectin concentration forming more brittle gels, (b) an increase in gel strength with increasing gluten content forming more elastic gels and (c) interactions of low-ester pectin with the gluten network, with pectin addition causing increased aggregation of gluten, leading to strengthened networks. Full article

The dramatic increase in urban construction waste poses severe environmental challenges. Utilizing waste concrete to produce recycled aggregates (RA) for manufacturing recycled concrete (RC) represents an effective strategy for resource utilization. However, inherent defects in RA, such as high porosity, microcracks, and adherent old mortar layers, lead to significant performance degradation of the resulting RC, limiting its widespread application. Traditional methods for enhancing RA often suffer from limitations, including high energy consumption, increased costs, or the introduction of new pollutants. MICP offers an innovative approach for enhancing RC performance. This technique employs the metabolic activity of specific microorganisms to induce the formation of a three-dimensionally interwoven calcium carbonate gel network within the pores and on the surface of RA. This gel network can improve the inherent defects of RA, thereby enhancing the performance of RC. Compared to conventional techniques, this approach demonstrates significant environmental benefits and enhances concrete compressive strength by 5–30%. Furthermore, embedding mineralizing microbial spores within the pores of RA enables the production of self-healing RC. This review systematically explores recent research advances in microbial mineral gel network for improving RC performance. It begins by delineating the fundamental mechanisms underlying microbial mineralization, detailing the key biochemical reactions driving the formation of calcium carbonate (CaCO₃) gel, and introducing the common types of microorganisms involved. Subsequently, it critically discusses the key environmental factors influencing the effectiveness of MICP treatment on RA and strategies for their optimization. The analysis focuses on the enhancement of critical mechanical properties of RC achieved through MICP treatment, elucidating the underlying strengthening mechanisms at the microscale. Furthermore, the review synthesizes findings on the self-healing efficiency of MICP-based RC, including such metrics as crack width healing ratio, permeability recovery, and restoration of mechanical properties. Key factors influencing self-healing effectiveness are also discussed. Finally, building upon the current research landscape, the review provides perspectives on future research directions for advancing microbial mineralization gel techniques to enhance RC performance, offering a theoretical reference for translating this technology into practical engineering applications. Full article

Ecological restoration in the cold and high-altitude mining areas of the Qinghai–Tibet Plateau is faced with dual challenges of extreme environments and insufficient microbial adaptability. This study aimed to screen local microbial resources with both extreme environmental adaptability and plant-growth-promoting functions. Local fungi (DK; F18-3) and commercially available bacteria (B0) were used as materials to explore their regulatory mechanisms for plant growth, soil physicochemical factors, microbial communities, and metabolic profiles in the field. Compared to bacterial treatments, local fungi treatments exhibited stronger ecological restoration efficacy. In addition, the DK and F18-3 strains, respectively, increased shoot and root biomass by 23.43% and 195.58% and significantly enhanced soil nutrient content and enzyme activity. Microbiome analysis further implied that, compared with the CK, DK treatment could significantly improve the α-diversity of fungi in the rhizosphere soil (the Shannon index increased by 14.27%) and increased the amount of unique bacterial genera in the rhizosphere soil of plants, totaling fourteen genera. Meanwhile, this aggregated the most biomarkers and beneficial microorganisms and strengthened the interactions among beneficial microorganisms. After DK treatment, twenty of the positively accumulated differential metabolites (DMs) in the plant rhizosphere were highly positively associated with six plant traits such as shoot length and root length, as well as beneficial microorganisms (e.g., Apodus and Pseudogymnoascus), but two DMs were highly negatively related to plant pathogenic fungi (including Cistella and Alternaria). Specifically, DK mainly inhibited the growth of pathogenic fungi through regulating the accumulation of D-(+)-Malic acid and Gamma-Aminobutyric acid (Cistella and Alternaria decreased by 84.20% and 58.53%, respectively). In contrast, the F18-3 strain mainly exerted its antibacterial effect by enriching Acidovorax genus microorganisms. This study verified the core role of local fungi in the restoration of mining areas in the Qinghai–Tibet Plateau and provided a new direction for the development of microbial agents for ecological restoration in the Qinghai–Tibet Plateau. Full article

Urban mobility resilience plays a central role in sustainable urban planning discussions, especially considering the challenges of extreme events, climate change, and the increasing scarcity of fossil fuels. This study evaluates urban mobility resilience in Petrópolis (RJ), incorporating socio-spatial heterogeneity and energy vulnerability. This research fills methodological gaps in the literature by proposing a composite resilience index that integrates technical, socioeconomic, and fossil fuel dependency variables within a robust multicriteria framework. We selected eleven variables relevant to urban mobility and organized them into inference blocks. We normalized the variables using Gaussian functions, respecting their maximization or minimization characteristics. We applied the Analytic Hierarchy Process (AHP) to assign weights to the criteria and then aggregated and ranked the results using multicriteria analysis. The final index represents the adaptive capacity of urban territories facing the energy crisis, and we applied it spatially to the neighborhoods of Petrópolis. The analysis identified a significant concentration of neighborhoods with low resilience, particularly in quadrants, combining deficiencies in public transportation, high dependence on fossil fuels, and socioeconomic constraints. Factors such as limited pedestrian access, insufficient motorized public transport coverage, and a high proportion of elderly residents emerged as significant constraints on urban resilience. Intervention strategies that promote active mobility, improve accessibility, and diversify transportation modes proved essential for strengthening local resilience. The results emphasize the urgent need for public policies to reduce energy vulnerability, foster active mobility, and promote equity in access to transportation infrastructure. Full article

Improper regulation of sugarcane feed volume can lead to harvester inefficiency or clogging. Accurate recognition of feed volume is therefore critical. However, visual recognition is challenging due to sugarcane stacking during feeding. To address this, we propose YOLO-ASM (YOLO Accurate Stereo Matching), a novel detection method. At the target detection level, we integrate a Convolutional Block Attention Module (CBAM) into the YOLOv5s backbone network. This significantly reduces missed detections and low-confidence predictions in dense stacking scenarios, improving detection speed by 28.04% and increasing mean average precision (mAP) by 5.31%. At the stereo matching level, we enhance the SGBM (Semi-Global Block Matching) algorithm through improved cost calculation and cost aggregation, resulting in Opti-SGBM (Optimized SGBM). This double-cost fusion approach strengthens texture feature extraction in stacked sugarcane, effectively reducing noise in the generated depth maps. The optimized algorithm yields depth maps with smaller errors relative to the original images, significantly improving depth accuracy. Experimental results demonstrate that the fused YOLO-ASM algorithm reduces sugarcane volume error rates across feed volumes of one to six by 3.45%, 3.23%, 6.48%, 5.86%, 9.32%, and 11.09%, respectively, compared to the original stereo matching algorithm. It also accelerates feed volume detection by approximately 100%, providing a high-precision solution for anti-clogging control in sugarcane harvester conveyor systems. Full article

In order to address a challenging issue in the recycling of construction debris, the impact of carbonization treatment on the characteristics of recycled aggregates (RCAs) was experimentally examined in this work. Both direct carbonization and carbonization following calcium hydroxide pretreatment were used in the study to assess the impact of carbonization on the physical characteristics of recycled aggregates. According to the findings, carbonization raised the recycled aggregates’ apparent density while drastically lowering their porosity and water absorption (by as much as 20–30%). Although the recycled aggregate’s crushing index marginally increased with age, its overall physical qualities remained excellent. Pretreatment with calcium hydroxide can improve the physical characteristics of recycled aggregates, further optimize their pore structure, and efficiently encourage the carbonation process. Furthermore, recycled aggregate’s crushing index can be considerably decreased and its quality much enhanced by the ultrasonic cavitation treatment. According to the study, the carbonation-treated recycled aggregate’s microstructure was denser in the interfacial transition zone and had a stronger link with the cement paste, improving the recycled aggregate concrete’s overall performance. XRD, infrared spectral analysis, and SEM scanning were used to determine the increased calcium carbonate content in the recycled aggregate following carbonation treatment as well as its microstructure improvement process. The findings offer fresh concepts for achieving resource efficiency and environmental preservation through the use of recycled aggregates in concrete, as well as theoretical backing for their use. Full article

The transformative trajectory of urban development in the contemporary era has engendered a substantial escalation in construction waste generation, particularly in China, where it constitutes approximately 40% of the total solid waste stream. Traditional landfill disposal methodologies pose formidable ecological challenges, encompassing soil contamination, groundwater pollution, and significant greenhouse gas emissions. Furthermore, the unsustainable exploitation of natural sandstone resources undermines energy security and disrupts ecological balance. In response to these pressing issues, an array of scholars and researchers have embarked on an exploratory endeavor to devise innovative strategies for the valorization of construction waste. Among these strategies, the conversion of waste into recycled aggregates has emerged as a particularly promising pathway. However, the practical deployment of recycled aggregates within the construction industry is impeded by their inherent physico-mechanical properties, such as heightened water absorption capacity and diminished compressive strength. To surmount these obstacles, a multitude of enhancement techniques, spanning physical, chemical, and thermal treatments, have been devised and refined. This paper undertakes a comprehensive examination of the historical evolution, recycling methodologies, and enhancement strategies pertinent to recycled aggregates. It critically evaluates the efficacy, cost–benefit analyses, and environmental ramifications of these techniques, while elucidating the microstructural and physicochemical disparities between recycled and natural aggregates. Furthermore, it identifies pivotal research gaps and prospective avenues for future inquiry, underscoring the imperative for collaborative endeavors aimed at developing cost-effective and environmentally benign enhancement techniques that adhere to the stringent standards of contemporary construction practices, thereby addressing the intertwined challenges of waste management and resource scarcity. Full article

Aiming at the problem of appearance drift and susceptibility to noise interference in visual place recognition (VPR), we propose DD–DPFE: a Dynamic Difference and Dual-Path Feature Enhancement method. Embedding differential attention mechanisms in the DINOv2 model to mitigate the effects of process interference and adding serial-parallel adapters allows efficient model parameter migration and task adaptation. Our method constructs a two-way feature enhancement module with global–local branching synergy. The global branch employs a dynamic fusion mechanism with a multi-layer Transformer encoder to strengthen the structured spatial representation to cope with appearance changes, while the local branch suppresses the over-response of redundant noise through an adaptive weighting mechanism and fuses the contextual information from the multi-scale feature aggregation module to enhance the robustness of the scene. The experimental results show that the model architecture proposed in this paper is an obvious improvement in different environmental tests. This is most obvious in the simulation test of a night scene, verifying that the proposed method can effectively enhance the discriminative power of the system and its anti-jamming ability in complex scenes. Full article

In arid and semi-arid areas, soil is blown up by the wind because of its loose structure. Wind erosion causes soil quality and fertility loss, land degradation, air pollution, disruption of ecological balance, and agricultural and livestock losses. Consequently, there is an immediate imperative for methods to mitigate the impacts of wind erosion. SICP (soybean urease-induced carbonate precipitation) has emerged as a promising biogeotechnical technology in mitigating wind erosion in arid and semi-arid regions. To enhance bio-cementation efficacy and treatment efficiency of SICP, aluminum chloride (AlCl₃) was employed as an additive to strengthen the SICP process. Multiple SICP treatment cycles with AlCl₃ additive were conducted on Tengger Desert sand specimens, with the specimens treated without AlCl₃ as the control group. The potential mechanisms by which AlCl₃ enhances SICP may have two aspects: (1) its flocculation effect accelerates the salting-out of proteinaceous organic matter in the SICP solution, retaining these materials as nucleation sites within soil pores; (2) the highly charged Al³⁺ cations adsorb onto negatively charged sand particle surfaces, acting as cores to attract and coalesce free CaCO₃ in solution, thereby promoting preferential precipitation at particle surfaces and interparticle contacts. This mechanism enhances CaCO₃ cementation efficiency, as evidenced by 2.69–3.89-fold increases in penetration resistance at the optimal 0.01 M AlCl₃ concentration, without reducing CaCO₃ production. Wind erosion tests showed an 88% reduction in maximum erosion rate (from 1142.6 to 135.3 g·m⁻²·min⁻¹), directly correlated with improved microstructural density observed via SEM (spherical CaCO₃ aggregates at particle interfaces). Economic analysis revealed a 50% cost reduction due to fewer treatment cycles, validating the method’s sustainability. These findings highlight AlCl₃-modified SICP as a robust, cost-effective strategy for wind erosion control in arid zones, with broad implications for biogeotechnical applications. Full article

Mine ecological restoration is a critical process for promoting the sustainable development of resource-dependent regions, yet existing monitoring methods remain limited in accuracy and adaptability. To address challenges such as small-object recognition, insufficient multi-scale feature fusion, and blurred boundaries in UAV-based remote sensing imagery, this paper proposes an enhanced semantic segmentation model based on Segformer. Specifically, a multi-scale feature-enhanced feature pyramid network (MSFE-FPN) is introduced between the encoder and decoder to strengthen cross-level feature interaction. Additionally, a selective feature aggregation pyramid pooling module (SFA-PPM) is integrated into the deepest feature layer to improve global semantic perception, while an efficient local attention (ELA) module is embedded into lateral connections to enhance sensitivity to edge structures and small-scale targets. A high-resolution UAV image dataset, named the HUNAN Mine UAV Dataset (HNMUD), is constructed to evaluate model performance, and further validation is conducted on the public Aeroscapes dataset. Experimental results demonstrated that the proposed method exhibited strong performance in terms of segmentation accuracy and generalization ability, effectively supporting the image analysis needs of mine restoration scenes. Full article

In surface energy tests of asphalt materials, the inaccuracy of the calculation method (e.g., least squares (LS)) and the arbitrary selection of chemical reagent combinations lead to unstable results, threatening the quantitative evaluation of asphalt–aggregate adhesion durability. This study addresses these two scientific deficiencies with the following findings: (1) when simultaneous equations are used to calculate the asphalt surface energy parameters, the total least squares method should be used instead of the classical least squares method to reduce the fitting error; (2) the selection of the reagent combination should be based on which one is the most rational in terms of the physical characterization, leap degree, abnormal values, and other requirements, and the reagent combination with the fewest abnormal values should be chosen as the best scheme. The results show that (1) compared with the classical least squares method, the total least squares method reduces the fitting error between the calculated and real values of asphalt surface energy parameters and improves the accuracy and stability of the calculation results; (2) the best reagent combination scheme is WFSD (distilled water + formamide + dimethyl sulfoxide + diiodomethane). The calculated values of asphalt surface energy parameters were more accurate and reasonable, and the calculation results had no abnormal values. Compared with WFEG (distilled water + formamide + ethylene glycol + glycerol), the error rate of the reagent combination scheme WFSD in calculating the total surface energy of two kinds of asphalt was reduced by 17.71% and 64.80%, respectively. These findings establish a reliable framework for the accurate quantification of surface energy, addressing the critical issue of reagent-dependent variability in the results and strengthening the scientific basis for evaluating the durability of asphalt pavement. Full article

Microalgae–fungal pellets (MFPs) effectively degrade pollutants in high-density aquaculture wastewater; however, their structural instability limits their long-term applicability. This study evaluated the effects of three crosslinking agents, sodium alginate (SA), chitosan (CTS), and polyvinyl alcohol (PVA), on enhancing the stability of MFPs. The results demonstrated that the initial 20 g/L SA-crosslinked MFP sample (SMFP₀) exhibited significantly improved structural stability, maintaining superior mechanical hardness (57.05 g) after 9 days. Further analysis revealed that SMFP₀ exhibited a more negative absolute Zeta potential (−13.05 mV), increased fluorescence intensity (0.020) in its tightly bound extracellular polymeric substances (TB-EPSs), and significantly higher protein (PN, 64.22 mg/L) and polysaccharide (PS, 56.99 mg/L) concentrations compared with the control (p < 0.05). These findings suggest that SMFP₀ possesses physicochemical properties that are conducive to microalgae–fungal aggregation. A scanning electron microscopy (SEM) analysis confirmed that the SA gel network enhanced the system’s stability by strengthening the microalgae–fungal interfacial adhesion and maintaining a porous, light-permeable structure. In practical wastewater treatment, SMFP₀ achieved superior removal rates for COD (84.19%), ammonia nitrogen (95.29%), total nitrogen (89.50%), and total phosphorus (93.46%) compared with non-crosslinked MFPs (p < 0.05). After 9 days of continuous operation (SMFP₉), the pollutant removal efficiencies remained comparable to those observed in the initial stage of the non-crosslinked system, indicating improved structural durability for extended practical application. Full article

Cyber threats that target Internet of Things (IoT) and edge computing environments are growing in scale and complexity, which necessitates the development of security solutions that are both robust and scalable while also protecting privacy. Edge scenarios require new intrusion detection solutions because traditional centralized intrusion detection systems (IDSs) lack in the protection of data privacy, create excessive communication overhead, and show limited contextual adaptation capabilities. This paper introduces the SecFedDNN framework, which combines federated deep learning (FDL) capabilities to protect edge–cloud environments from cyberattacks such as Distributed Denial of Service (DDoS), Denial of Service (DoS), and injection attacks. SecFedDNN performs edge-level pre-aggregation filtering through Layer-Adaptive Sparsified Model Aggregation (LASA) for anomaly detection while supporting balanced multi-class evaluation across federated clients. A Deep Neural Network (DNN) forms the main model that trains concurrently with multiple clients through the Federated Averaging (FedAvg) protocol while keeping raw data local. We utilized Google Cloud Platform (GCP) along with Google Colaboratory (Colab) to create five federated clients for simulating attacks on the TON_IoT dataset, which we balanced across selected attack types. Initial tests showed DNN outperformed Long Short-Term Memory (LSTM) and SimpleNN in centralized environments by providing higher accuracy at lower computational costs. Following federated training, the SecFedDNN framework achieved an average accuracy and precision above 84% and recall and F1-score above 82% across all clients with suitable response times for real-time deployment. The study proves that FDL can strengthen intrusion detection across distributed edge networks without compromising data privacy guarantees. Full article

Semantic segmentation plays a critical role in medical image analysis, offering indispensable information for the diagnosis and treatment planning of liver diseases. However, due to the complex anatomical structure of the liver and significant inter-patient variability, the current methods exhibit notable limitations in feature extraction and fusion, which pose a major challenge to achieving accurate liver segmentation. To address these challenges, this study proposes an improved U-Net-based liver semantic segmentation method that enhances segmentation performance through optimized feature extraction and fusion mechanisms. Firstly, a multi-scale input strategy is employed to account for the variability in liver features at different scales. A multi-scale convolutional attention (MSCA) mechanism is integrated into the encoder to aggregate multi-scale information and improve feature representation. Secondly, an atrous spatial pyramid pooling (ASPP) module is incorporated into the bottleneck layer to capture features at various receptive fields using dilated convolutions, while global pooling is applied to enhance the acquisition of contextual information and ensure efficient feature transmission. Furthermore, a Channel Transformer module replaces the traditional skip connections to strengthen the interaction and fusion between encoder and decoder features, thereby reducing the semantic gap. The effectiveness of this method was validated on integrated public datasets, achieving an Intersection over Union (IoU) of 0.9315 for liver segmentation tasks, outperforming other mainstream approaches. This provides a novel solution for precise liver image segmentation and holds significant clinical value for liver disease diagnosis and treatment. Full article